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The early detection of animal diseases can benefit farm productivity, by allowing the farmer to treat and isolate the affected animal as soon as the symptoms appear. This can be very effective at reducing the spread of disease to other animals. Sounds emitted by pigs, such as coughing, can be analyzed for disease detection. A computational ...
FlowerChecker, also known as Kindwise, [1] is a company that uses machine learning to identify natural objects from images. This includes plants and their diseases, but also insects and mushrooms. [2] [3] [4] It is based in Brno, Czech Republic. It was founded in 2014 by Ondřej Veselý, Jiří Řihák, and Ondřej Vild, at the time Ph.D. students.
Decisions may be based on decision-support models (crop simulation models and recommendation models) based on big data, but in the final analysis it is up to the farmer to decide in terms of business value and impacts on the environment- a role being taken over by artificial intelligence (AI) systems based on machine learning and artificial ...
This is a list of datasets for machine learning research. It is part of the list of datasets for machine-learning research. These datasets consist primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification.
CropSyst, a multi-year multi-crop daily time-step crop simulation model developed by a team at Washington State University's Department of Biological Systems Engineering. [ 4 ] DSSAT , the Decision Support System for Agro-technology Transfer, is a multi-crop, multi-year crop simulation model which evolved from the IBSNAT (1982-1993) and ICASA ...
When working on the field, the machine uses an AI-enabled detection feature to actuate blades around and in-between the crops and in the furrows. [21] In order for their weeding system to operate successfully, the team trained machine learning algorithms on millions of crop images, so that the machine can differentiate between crops and weeds ...
Satellite crop monitoring technology allows to perform online crop monitoring on different fields, located in different areas, regions, even countries and on different continents. The technology's advantage is a high automation level of sown area condition and its interpretation in an interactive map which can be read by different groups of users.
Emerging digital technologies have the potential to be game-changers for traditional agricultural practices. The Food and Agriculture Organization of the United Nations has referred to this change as a revolution: "a 'digital agricultural revolution' will be the newest shift which could help ensure agriculture meets the needs of the global population into the future."